# JosephDiPietro.github.io
## CMSC320 Final Project
## Introduction
####Human Trafficking has always been a major problem in the world and it has devastating effects on its victims.  With new databases we can examine risk factors in order to help mitigate this problem and counter the abusers.  In this project I will be examing the age of the victims, knowledge of the abuser, type of abuse used for trafficking, and whether the victim was abducted or not.  The data that I will use comes from the CTDC and contains information from all over the world.  In order to keep a reasonable scope for this project the data has been pre-processed to include online United States cases.  These cases range from years 2015 to 2018.  Using data analytics we can predict whether abusers are more violent or have a closer relation to the victim based on factors like age.

import pandas as pd
import seaborn as sea
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import numpy as np
import folium
from folium.plugins import MarkerCluster

globalFrame = pd.read_csv("trafficking.csv")
globalFrame = globalFrame.drop(globalFrame.columns[0],1)
globalFrame = globalFrame.drop(["Datasource","ageBroad","majorityStatus","majorityEntry"],1)
globalFrame = globalFrame.rename(columns = {"yearOfRegistration":"year","majorityStatusAtExploit":"majorityStatus","meansOfControlDebtBondage":"DebtBondage",
                                           "meansOfControlTakesEarnings":"EarningsStolen","meansOfControlRestrictsFinancialAccess": "WithholdsMoney",
                                           "meansOfControlThreats":"Threats","meansOfControlPsychologicalAbuse":"PsychologicalAbuse",
                                           "meansOfControlPhysicalAbuse":"PhysicalAbuse","meansOfControlSexualAbuse":"SexualAbuse",
                                           "meansOfControlFalsePromises":"FalsePromises","meansOfControlPsychoactiveSubstances":"PsychoactiveSubstances",
                                           "meansOfControlRestrictsMovement":"RestrictsMovement","meansOfControlRestrictsMedicalCare":"RestrictsMedicalCare",
                                           "meansOfControlExcessiveWorkingHours":"ExcessiveWorkingHours","meansOfControlUsesChildren":"UsesChildren",
                                           "meansOfControlThreatOfLawEnforcement":"ThreatOfLawEnforcement","meansOfControlWithholdsNecessities":"WithholdsNecessities",
                                           "meansOfControlWithholdsDocuments":"WithholdsDocuments","meansOfControlOther":"OtherControl","meansOfControlNotSpecified":"ControlNotSpecified",
                                           "recruiterRelationIntimatePartner":"IntimatePartner","recruiterRelationFriend":"Friend","recruiterRelationFamily":"Family",
                                           "recruiterRelationOther":"OtherRelation","recruiterRelationUnknown":"UnknownRelation"})
frame = globalFrame[globalFrame['citizenship'] == "US"]
frame = frame.reset_index()
frame = frame.drop('index',1)
frame.head()
frame["Cases"] = 1
frame.loc[frame['majorityStatus'] == '0', 'majorityStatus'] = "unkown"
ageFrame = frame.groupby(["majorityStatus","year"]).sum()
ageFrame['Cases'].plot.bar()
ageFrame = ageFrame.reset_index()
ageFrame

/opt/conda/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (6,55,57) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

ageFrame = ageFrame[ageFrame['majorityStatus'] != "unkown"]
physcialAbuse = sea.scatterplot(x = "year",y = "PhysicalAbuse",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()
sexualAbuse = sea.scatterplot(x = "year",y = "SexualAbuse",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()
pschologicalAbuse = sea.scatterplot(x = "year",y = "PsychologicalAbuse",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()
psychoactiveDrugs = sea.scatterplot(x = "year",y = "PsychoactiveSubstances",hue = "majorityStatus",data = ageFrame)
plt.show()
plt.clf()

<Figure size 432x288 with 0 Axes>

predictFrame = frame[frame["majorityStatus"] != "unkown"]
for index,row in predictFrame.iterrows():
    if(row.majorityStatus == "Minor"):
        predictFrame.loc[index,"BinaryAge"] = 1
    else:
        predictFrame.loc[index,"BinaryAge"] = 0
predictAge = predictFrame[["year","BinaryAge"]]
model = LogisticRegression()
model.fit(X = predictAge.drop("BinaryAge",1),y = predictAbuse["BinaryAge"])
model.predict(np.array([[2017]]))
predictFrame

/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1596: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self.obj[key] = _infer_fill_value(value)
/opt/conda/lib/python3.8/site-packages/pandas/core/indexing.py:1765: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  isetter(loc, value)

predictAbuse = predictFrame[["year","DebtBondage","EarningsStolen","WithholdsMoney","Threats","PhysicalAbuse","SexualAbuse","FalsePromises",
                            "PsychoactiveSubstances","RestrictsMovement","RestrictsMedicalCare","ExcessiveWorkingHours",
                            "UsesChildren","ThreatOfLawEnforcement","WithholdsNecessities","WithholdsDocuments",
                            "OtherControl","ControlNotSpecified","BinaryAge"]]
model = LinearRegression()
model.fit(X = predictAbuse.drop("BinaryAge",1),y = predictAbuse["BinaryAge"])
print("A victim of threats with 1 being a minor in 2017 is",model.predict(np.array([[2017,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0]])))

A victim of threats with 1 being a minor in 2017 is [0.77239109]

predictRecruit = predictFrame[["year","IntimatePartner","Family","OtherRelation","UnknownRelation","BinaryAge"]]
model = LinearRegression()
model.fit(X = predictRecruit.drop("BinaryAge",1),y = predictAbuse["BinaryAge"])
print("A victim of a family member with one being a minor in 2017 is",model.predict(np.array([[2017,0,1,0,0]])))
print("A victim of an intimate partner with one being a minor in 2015 is",model.predict(np.array([[2015,1,0,0,0]])))

A victim of a family member with one being a minor in 2017 is [0.96826561]
A victim of an intimate partner with one being a minor in 2015 is [0.71835606]

m = folium.Map()
cluster = MarkerCluster().add_to(m)
for index,row in globalFrame.iterrows():
    if(row.citizenship == "CO"):
        folium.map.Marker(location = ['4.5709','-74.2973'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "MD"):
        folium.map.Marker(location = ['47.4116','28.3699'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "RO"):
        folium.map.Marker(location = ['45.9432','24.9668'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "UA"):
        folium.map.Marker(location = ['48.3794','31.1656'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "BY"):
        folium.map.Marker(location = ['53.7098','27.9534'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "HT"):
        folium.map.Marker(location = ['18.9712','-72.2852'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "UZ"):
        folium.map.Marker(location = ['41.3775','64.5853'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "LK"):
        folium.map.Marker(location = ['7.8731','80.7718'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "LK"):
        folium.map.Marker(location = ['7.8731','80.7718'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "MM"):
        folium.map.Marker(location = ['21.9162','95.9560'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "UG"):
        folium.map.Marker(location = ['1.3733','32.2903'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "ID"):
        folium.map.Marker(location = ['-0.7893','113.9213'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "ID"):
        folium.map.Marker(location = ['-0.7893','113.9213'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "KG"):
        folium.map.Marker(location = ['42.882004','74.582748'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "AF"):
        folium.map.Marker(location = ['33.9391','67.7100'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "ER"):
        folium.map.Marker(location = ['15.1794','39.7823'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "NG"):
        folium.map.Marker(location = ['17.6078','8.0817'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "NP"):
        folium.map.Marker(location = ['28.3949','84.1240'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "PH"):
        folium.map.Marker(location = ['12.8797','121.7740'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "KH"):
        folium.map.Marker(location = ['12.5657','104.9910'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "BD"):
        folium.map.Marker(location = ['23.6850','90.3563'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "US"):
        folium.map.Marker(location = ['37.0902','-95.7129'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "TH"):
        folium.map.Marker(location = ['15.8700','100.9925'],popup = row.citizenship).add_to(cluster)
    if(row.citizenship == "VN"):
        folium.map.Marker(location = ['14.0583','108.2772'],popup = row.citizenship).add_to(cluster)
m

	year	gender	majorityStatus	citizenship	DebtBondage	EarningsStolen	WithholdsMoney	Threats	PsychologicalAbuse	PhysicalAbuse	...	isAbduction	RecruiterRelationship	CountryOfExploitation	IntimatePartner	Friend	Family	OtherRelation	UnknownRelation	Cases	BinaryAge
0	2015	Female	Minor	US	0	0	0	0	0	0	...	0	Intimate Partner	US	1	0	0	0	0	1	1.0
1	2015	Female	Adult	US	0	0	0	0	1	0	...	0	Not Specified	US	0	0	0	0	1	1	0.0
2	2015	Female	Minor	US	0	0	0	1	0	1	...	0	Friend/Acquaintance	US	0	1	0	0	0	1	1.0
6	2015	Female	Minor	US	0	0	0	1	0	0	...	0	Other	US	0	0	0	1	0	1	1.0
7	2015	Female	Minor	US	1	1	0	0	1	1	...	0	Intimate Partner	US	1	0	0	0	0	1	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3631	2018	Male	Minor	US	0	0	0	0	1	0	...	0	Family/Relative	US	0	0	1	0	0	1	1.0
3632	2018	Male	Minor	US	0	0	0	1	1	1	...	0	Family/Relative	US	0	0	1	0	0	1	1.0
3633	2018	Male	Minor	US	0	0	0	1	1	1	...	0	Family/Relative	US	0	0	1	0	0	1	1.0
3634	2018	Male	Minor	US	0	0	0	0	0	0	...	0	Family/Relative	US	0	0	1	0	0	1	1.0
3635	2018	Male	Minor	US	0	0	0	0	1	1	...	0	Family/Relative	US	0	0	1	0	0	1	1.0